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MTG: A Benchmark Suite for Multilingual Text Generation

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arxiv 2108.07140 v2 pith:V6NZWM2P submitted 2021-08-13 cs.CL

MTG: A Benchmark Suite for Multilingual Text Generation

Yiran Chen , Zhenqiao Song , Xianze Wu , Danqing Wang , Jingjing Xu , Jiaze Chen , Hao Zhou , Lei Li This is my paper
classification cs.CL
keywords generationmultilingualtextbenchmarkdatasuiteacrosshuman-annotated
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation. It is the first-proposed multilingual multiway text generation dataset with the largest human-annotated data (400k). It includes four generation tasks (story generation, question generation, title generation and text summarization) across five languages (English, German, French, Spanish and Chinese). The multiway setup enables testing knowledge transfer capabilities for a model across languages and tasks. Using MTG, we train and analyze several popular multilingual generation models from different aspects. Our benchmark suite fosters model performance enhancement with more human-annotated parallel data. It provides comprehensive evaluations with diverse generation scenarios. Code and data are available at \url{https://github.com/zide05/MTG}.

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